Published in Artificial Intelligence Review, the study reviews recent advances in Green AI methodologies and introduces the Green AI Urban Framework, or GAUF, a structured conceptual framework for embedding environmentally responsible AI into sustainable urban systems. The authors, led by Elda Cina and colleagues, survey techniques across four fronts: energy-efficient model design, lightweight and adaptive learning, data-efficient training, and resource-aware deployment strategies. Each addresses a different stage of the AI lifecycle, from how models are built to where they run.

The survey examines major urban application domains—electricity demand prediction, water resource optimization, waste management and smart mobility—to identify current capabilities, limitations and research gaps. The analysis is a consolidation of existing literature rather than a new empirical evaluation, so it maps what has been demonstrated in prior work rather than reporting fresh performance results. Its value lies in organizing a fragmented field: green AI techniques exist, but until now there has been no unified structure for applying them specifically to urban infrastructure.

GAUF integrates three core components. The first is sustainability-aware model selection, which weighs environmental cost alongside predictive accuracy when choosing algorithms. The second covers cloud–edge deployment strategies, distributing computation between centralized data centers and local edge devices to reduce energy overhead and latency. The third establishes energy-efficiency evaluation metrics that span the entire AI lifecycle, from training through inference, so that a model’s environmental footprint can be measured rather than assumed.

Beyond the technical core, the framework incorporates implementation and governance considerations relevant to urban infrastructure, sustainability reporting and municipal adoption. This acknowledges a practical reality: cities do not simply deploy algorithms, they procure, regulate and audit them. A framework that ignores procurement rules or reporting obligations would struggle to move from paper to practice, and the authors position GAUF as a bridge between AI researchers, practitioners and policymakers.

The broader tension the paper highlights is genuine. AI’s computational complexity and energy consumption have grown alongside its capabilities, creating what the authors describe as new sustainability challenges within systems designed to solve sustainability problems. An electricity demand forecast that saves megawatt-hours on the grid may, if trained and served inefficiently, consume a meaningful share of those savings itself. Quantifying that trade-off requires the kind of lifecycle metrics GAUF proposes, which the survey identifies as a current research gap.

As a conceptual contribution, GAUF does not report measured energy savings or benchmarked deployments; its claims are about structure and direction, not demonstrated outcomes. The authors state its aim explicitly: to support the design of AI-driven urban systems that balance predictive performance, scalability and environmental responsibility. By consolidating existing research and outlining future directions, the study positions sustainable AI practice as a core component of next-generation smart and resilient cities, while leaving empirical validation of the framework to future work.

Subject of Research: A green AI framework and survey for environmentally sustainable smart city systems

Article Title: Designing sustainable urban intelligence: a green AI framework and survey

Article References: Cina, E., AlArnaout, Z., Elbasi, E., & Elmazi, G. (2026). Designing sustainable urban intelligence: a green AI framework and survey. Artificial Intelligence Review. https://doi.org/10.1007/s10462-026-11730-3

Image Credits: AI Generated

DOI: 10.1007/s10462-026-11730-3

Keywords: Green AI, smart cities, urban sustainability, energy-efficient AI, cloud-edge computing, electricity demand prediction, water resource optimization, waste management, smart mobility, AI governance, sustainability metrics, Designing

The framework, called SMWPF, was developed by Chenyu Jing, Xiaolong Xu, and Ran Li of Nanjing University of Information Science and Technology, together with Xin Liu of the Public Meteorological Service Center at the China Meteorological Administration and Muhammad Bilal of Lancaster University. Their starting point is a frustration familiar to anyone working with high-resolution meteorological data. Existing spatiotemporal deep learning models can fuse multiple atmospheric variables and perform reasonably well when the wind field is described at medium resolution. But when the data become finer-grained, those same models tend to produce wind fields that look suspiciously smooth, washing out the local detail that matters most for wind farms and for early warnings of extreme wind events. Over-smoothing is not a cosmetic flaw; it means the model averages away precisely the sharp gradients and localized bursts that determine whether a turbine cluster will be hammered by a squall or spared.

SMWPF attacks that problem with a division of labor. At the edge of the network, close to where the data are collected, a lightweight predictor generates forecasts with low latency. Speed is its virtue: edge devices cannot afford the computational luxury of a giant model, and in operational forecasting, minutes matter. But a small model has limited capacity, and its internal representation of the atmosphere can be coarse. The researchers address this by passing the edge model’s latent representations through a soft vector quantization step, implemented with a two-layer multilayer perceptron and an extended codebook. In essence, the network learns a dictionary of representative latent patterns and gently nudges the edge model’s internal states toward the most informative entries in that dictionary. This regularization discourages the blurry, averaged-out outputs that plague high-resolution prediction and helps restore local structure to the forecast wind field.

Another distinctive ingredient is the way the framework treats dangerous winds. Rather than asking its attention module to scan the entire domain uniformly, SMWPF fuses an extreme wind speed factor with elevation information, producing a guide that directs the model’s attention toward regions with a documented history of strong winds. Terrain matters enormously here: mountain passes, ridge lines, and coastal escarpments can accelerate airflow dramatically, and a model that knows where those hotspots are can allocate its representational resources accordingly. By embedding this historical and topographical knowledge directly into the attention mechanism, the architecture encodes a form of meteorological common sense that a generic spatiotemporal network would otherwise have to rediscover from scratch.

Perhaps the most intriguing element of the design is its honesty about uncertainty. The edge predictor does not simply emit a forecast and move on; the framework estimates the confidence of each prediction. Samples that pass the confidence threshold are accepted as final, delivered quickly and cheaply. Samples that fall short are escalated to the cloud, where a predictor built on Qwen, a large language model, takes over. The cloud model maps the original normalized historical meteorological tensor directly to the future wind field, effectively re-predicting the difficult cases with far greater capacity. This is the large-small model collaboration at the heart of the system: the small model handles the routine workload at the edge, while the large model is reserved for the genuinely hard cases, and the routing between them is driven by measured confidence rather than a fixed rule.

The economics of this arrangement are as interesting as the science. Running a large model on every forecast would be prohibitively expensive and slow, while relying solely on a small model would sacrifice accuracy at the moments when accuracy matters most, namely during complex, extreme, or rapidly evolving conditions. A confidence-gated escalation path offers a middle way that mirrors how human institutions already work: a junior forecaster handles routine bulletins and escalates the ambiguous ones to a senior expert. In SMWPF, the escalation happens automatically, in milliseconds, and the cloud’s greater computational muscle is spent only where it is likely to pay off. The architecture also reflects a broader trend in computing, in which intelligence is distributed across edge devices and data centers rather than concentrated in one place.

To find out whether the design actually works, the team evaluated SMWPF on both private and public datasets, comparing it against state-of-the-art spatiotemporal forecasting methods. The headline result is a reduction in root mean square error of five percent on average across the datasets. That may sound modest, but in the context of wind speed forecasting, where models have been refined for years and the atmosphere itself imposes hard limits on predictability, a consistent five percent improvement over the best existing methods is a meaningful advance. RMSE penalizes large errors heavily, so the metric is particularly sensitive to exactly the kind of localized, high-magnitude wind events that over-smoothed models tend to miss. Reducing it suggests that the framework’s anti-smoothing and attention-guiding mechanisms are doing real work rather than merely reshuffling error.

The implications extend beyond academic benchmarks. Grid operators must schedule reserve capacity, trade electricity in forward markets, and issue warnings to communities in the path of damaging winds, and all of those decisions improve as forecast error shrinks. A five percent reduction in RMSE, applied across thousands of forecast points and repeated every forecasting cycle, compounds into better turbine scheduling, fewer emergency interventions, and more confident integration of wind into power grids that were designed around dispatchable fuels. The framework’s emphasis on extreme wind events also speaks directly to safety: as climate change alters wind regimes and severe storms become costlier, early and accurate warning of locally intense winds is a growing operational requirement for meteorological services.

The study also offers a template that could generalize well beyond wind. The core pattern, a lightweight edge model augmented by vector quantization, guided by domain-informed attention, and backed by a confidence-triggered escalation to a large cloud model, is not specific to meteorology. Any prediction problem with high-resolution spatiotemporal data, tight latency constraints, and a heavy-tailed distribution of difficult cases could benefit from the same architecture: flood mapping, air quality forecasting, traffic prediction, and wildfire spread modeling all share those characteristics. The authors’ demonstration that a large language model can serve as the cloud-side re-predictor, consuming a normalized tensor of historical meteorological variables and producing a future wind field, is a notable data point in the ongoing convergence of foundation models with scientific forecasting.

There are, of course, caveats worth keeping in mind. The five percent figure is an average across datasets, and performance in any single deployment will depend on local terrain, sensor density, and the quality of historical records used to build the extreme wind factor. The confidence estimation that gates the cloud escalation is itself a learned component, and its calibration will determine how often the system escalates and how wisely. The research received no specific external funding, and the authors declare no competing interests. Published open access on 17 October 2026, the paper arrives as an early citable version subject to final edits. Even so, SMWPF makes a compelling case that the future of environmental forecasting lies not in choosing between small fast models and large powerful ones, but in designing systems that know when to use each, and that can quietly hand the hardest atmospheric puzzles from the edge of the network to the cloud, and back, faster than the wind can change its mind.

Subject of Research: Cloud-edge collaborative deep learning for high-resolution wind speed forecasting

Article Title: SMWPF: a cloud-edge collaborative network enabled by large-small model collaboration for wind speed prediction

Article References: Jing, C., Liu, X., Xu, X., Li, R., & Bilal, M. (2026). SMWPF: a cloud-edge collaborative network enabled by large-small model collaboration for wind speed prediction. Journal of Big Data. https://doi.org/10.1186/s40537-026-01570-x

Image Credits: AI Generated

DOI: 10.1186/s40537-026-01570-x

Keywords: wind speed forecasting, cloud-edge computing, large-small model collaboration, vector quantization, extreme wind events, machine learning, meteorology, wind energy, Qwen, attention mechanism, spatiotemporal prediction, Journal of Big Data

The core problem the researchers set out to solve is one that anyone deploying artificial intelligence in the real world eventually confronts. Most high-performing image segmentation models depend on heavy backbone networks—deep stacks of convolutional layers borrowed from image classification research—combined with intricate feature-fusion schemes that merge information from many layers of the network. These designs excel at benchmark accuracy, but they impose punishing demands on memory, processing power, and energy. On an edge device with restricted computing resources, such a model may take seconds or minutes to produce a single prediction, drain a battery, or simply fail to fit in available memory. When the alternative is shipping raw images over a network to a cloud server for processing, new bottlenecks appear in the form of inference latency and communication cost, both of which matter enormously in time-sensitive applications such as clinical screening.

LMANet’s answer to this dilemma is an encoder-attention-decoder architecture that distributes the computational burden intelligently. At the front of the pipeline sits an efficient convolution-based compact encoder, whose job is to extract basic visual features from the input image. Because this encoder is deliberately kept small, it can run on the edge side—the device where the image is captured—without overwhelming local hardware. The subsequent stages of the network, a multiscale attention module that enriches the extracted features and a decoder that converts them into a final pixel-level segmentation, can then be executed flexibly by the cloud side whenever additional computational support is needed. This division of labor is the essence of cloud-edge collaboration: the edge does the lightweight work that must happen close to the data, while the cloud handles the heavier aggregation and decoding steps on demand.

The technical heart of the design is the multiscale attention module, and understanding why it matters requires a brief look at how segmentation networks see. An image contains structures at many different scales: a skin lesion, for example, has fine boundary details that must be traced precisely, a broader regional texture that distinguishes diseased tissue from healthy skin, and an overall semantic identity that tells the network what the object in question actually is. A network that processes everything at a single scale tends to miss some of these cues. Attention mechanisms address this by letting the model learn where to focus—weighting certain features more heavily than others based on their relevance to the task. LMANet’s multiscale attention module is designed to strengthen all three categories of information simultaneously: boundary details, regional contextual information, and global semantic responses, without requiring the enormous parameter counts that heavy fusion modules typically demand.

To evaluate the approach, the team turned to the ISIC 2018 dataset, a widely used public benchmark of dermoscopy images for skin-lesion analysis. Skin-lesion segmentation is a demanding test case for any lightweight model, because the clinical value of the output depends on the accuracy of the lesion boundary: a segmentation that slightly overestimates or underestimates the extent of a lesion can change a subsequent diagnosis or biopsy decision. The task therefore sits at the intersection of the two pressures the study aims to reconcile—pixel-level accuracy on one side and the practical constraints of inference latency, model size, and communication cost on the other. It is precisely the kind of workload that a cloud-edge visual analytics system would be expected to handle in a real deployment.

The reported results are striking for a model of this size. On the ISIC 2018 benchmark, LMANet achieved a Dice score of 0.921 and an intersection-over-union, or IoU, of 0.853. The Dice score, which ranges from zero to one, measures the overlap between the predicted segmentation and the ground truth, with values above 0.9 generally considered excellent in medical imaging. IoU, a stricter overlap metric that penalizes both false positives and false negatives, at 0.853 indicates that the model’s predicted lesion regions agree closely with expert annotations. Achieving these numbers is not unusual for large, computationally expensive networks—but LMANet does so with only 5.6 million parameters and 9.4 gigaflops of computation, figures that place the model firmly in the lightweight category suitable for resource-constrained hardware.

For context, many mainstream segmentation networks carry parameter counts several times larger, sometimes exceeding tens of millions of parameters and requiring substantially more floating-point operations per inference. Every additional parameter must be stored in memory, moved through the processor, and, in a cloud-edge setting, potentially synchronized between devices. Every additional floating-point operation translates directly into inference time and energy consumption. By holding the parameter count to 5.6 million while maintaining a Dice score above 0.92, LMANet demonstrates that the accuracy-versus-efficiency curve can be shifted favorably when the architecture is designed around the deployment environment rather than adapted to it after the fact.

The authors report that, compared with representative segmentation methods, LMANet shows a more balanced performance across the three axes that matter in practice: segmentation accuracy, model compactness, and inference efficiency. This notion of balance is arguably the study’s most important contribution. In the research literature, headline accuracy figures often dominate the conversation, but a model that wins a benchmark by a fraction of a percentage point while requiring ten times the compute may be a poor choice for an actual deployment. By explicitly optimizing for the joint objective, the researchers offer a template for how segmentation models for cloud-edge visual analytics might be evaluated going forward—not as isolated accuracy numbers but as profiles across accuracy, size, and speed.

The implications extend beyond dermatology. The architectural principles at work in LMANet—compact encoders at the edge, attention-driven multiscale feature aggregation, and flexible cloud-side decoding—apply to any visual analytics scenario where images are captured on constrained devices and analyzed with the help of remote infrastructure. Smart-city cameras, agricultural drones, industrial inspection systems, and remote diagnostic tools in underserved regions all face versions of the same constraint: the intelligence must be accurate, but it must also fit within tight budgets of latency, bandwidth, and energy. A model that can be split between edge and cloud, performing basic feature extraction locally and deferring heavier processing to the cloud only when needed, offers a practical pathway for such systems to adopt deep learning without rebuilding their hardware.

The study, published open access in the Journal of Big Data on 5 October 2026, was supported by the Doctoral Research Foundation of Weifang University, the Shandong Provincial Natural Science Foundation, and Wonkwang University. The authors, led by Yujie Li of Weifang University’s School of Computer Engineering with corresponding author Sun-Kyoung Kang of Wonkwang University’s Department of Computer Software Engineering, note that the results demonstrate the effectiveness of LMANet for computationally efficient skin-lesion segmentation and indicate its potential for resource-constrained cloud-edge visual analytics more broadly. As artificial intelligence continues to migrate from data centers into the devices that surround us, work like this suggests that the future of applied machine learning may belong not to the largest models, but to the cleverest ones—networks that know exactly where to spend their limited computation, and where to ask for help.

Subject of Research: Lightweight attention-based neural networks for efficient cloud-edge image segmentation of skin lesions

Article Title: Cloud-edge efficient image segmentation via lightweight multiscale attention learning

Article References: Li, Y., Zhang, H., Liu, G., Wang, T., Chen, C., & Kang, S.-K. (2026). Cloud-edge efficient image segmentation via lightweight multiscale attention learning. Journal of Big Data. https://doi.org/10.1186/s40537-026-01577-4

Image Credits: AI Generated

DOI: 10.1186/s40537-026-01577-4

Keywords: image segmentation, cloud-edge computing, lightweight neural networks, attention mechanism, skin-lesion segmentation, ISIC 2018, Dice score, IoU, medical imaging, computer vision, deep learning, model efficiency